Electric-power companies use heat waste from a gas turbine or the like to provide heated water for district heating and to provide high-pressure steam and low-pressure steam for factories. Petrochemical companies operate a gas turbine and/or the like as a power supply facility. Early detection of an error generated in a gas turbine or the like utilized in various plants or facilities is vitally important, because the early error detection enables minimization of damage to the company.
There are facilities that require early detection of errors such as generated by deterioration, operative life and the like of, not only gas turbines and steam turbines, but also water wheels in hydroelectric power stations, nuclear reactors in nuclear power stations, windmills in wind power stations, engines of air vehicles or heavy vehicles, railway vehicles, escalators and elevators, and also even batteries mounted on devices/parts. Such facilities are too numerous to mention. Recently, for health maintenance, detection of errors (various disease presentations) in connection with human body is also becoming important, as seen in brain wave measurement and diagnosis.
To address this, for example, SmartSignal Corporation in USA carries out the business of detecting errors in, mainly, engines as described in U.S. Pat. No. 6,952,662 and U.S. Pat. No. 6,975,962. In these descriptions, past data is stored as database (DB), and the similarity between observation data and past training data is calculated by a unique method. Then, data with high similarity is linearly combined to calculate an estimate value. The degree of discrepancy between the estimate value and the observation data is output. As described by General Electric corporation, referring to the contents of U.S. Pat. No. 6,216,066, there is an example of use of k-means clustering to detect errors.